[Rupali Vashisht]

Dear Joao Santos Silva,

Thank you, this is very helpful!

Kind regards,
Rupali

[Cleo Rasmus]

Dear all,

I have a panel data with 52 countries stretching over 20 years. I ran 14 regressions with the same independent variables and different outcome variables. I used xtqreg with jackknife correction (bootstrapping with 20 replications) and without jackknife correction, and my results vary, but it is not clear that 1 approach gives more significant results across all 14 regressions. I was wondering, which is the better approach? Joao Santos Silva, in your paper, "quantiles via moments" you state that "In practice, the comparison between the MM-QR estimates and their bias-corrected counterparts should give a reasonable indication of the need to use the jackknife." To me it is not clear which model I should prefer, since the significance is not higher in one of the models, the coefficients and significance just vary. I also use a time fixed effect in my command (i.Year) in case this is relevant. Also, I read in your paper that for n/T up to 10 the jackknife correction does not need to be used. Am I correct to go for the model without jackknife correction then (since n/T is far below 10 for me)?

Further, for 1 of my regressions, I get a warning that more than 7% of the values of the scale function are negative. I read that always some values of the scale function are negative, but is 7% too much? Or, is there a way to reduce the percentage?

Thank you.
Kind regards,
Cleo

[Joao Santos Silva]

Dear Cleo Rasmus,

Indeed, you probably do not need the JK correction in this case. I would, however, use many more that 20 bootstrap replicas.

Best wishes,

Joao

[Cleo Rasmus]

Dear Joao Santos Silva,

thank you for your advice.

Best regards,
Cleo